Abstract
When users seek to find specific resources in a digital library, they often use the library catalog to locate them. These catalog queries are defined as known item queries. As known item queries search for specific resources, it is important to manage them differently from other search types, such as area searches. We study how to identify known item queries in the context of a large academic institution’s online public access catalog (OPAC), in which queries are issued via a simple keyword interface. We also examine how to recognize when a known item query has retrieved the item in question. Our approach combines techniques in machine learning, language modeling and machine translation evaluation metrics to build a classifier capable of distinguishing known item queries and correctly classifies titles for whether they are the known item sought with an 80% and 95% correlation to human performance, respectively on each task. To our knowledge, this is the first report of such work, which has the potential to streamline the user interface of both OPACs and digital libraries in support of known item searches.
Min-Yen Kan
Associate Professor
WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.